Jaehyun Shim
Papers
2
Total Citations
16
H-Index
2
About
Jaehyun Shim is a leading researcher in legged locomotion and whole-body control, with a focus on enabling robots to navigate complex, unstructured environments. His work bridges planning and control, introducing novel frameworks for multi-contact motion synthesis that allow robots to dynamically traverse large obstacles and industrial terrains. Shim’s 2024 paper on “Online Multicontact Receding Horizon Planning via Value Function Approximation” (11 citations) pioneers a method that uses learned value functions to guide real-time planning, effectively building momentum for challenging maneuvers—a critical advance over traditional prediction-horizon approaches. His 2025 study on “Perceptive Locomotion Through Whole-Body MPC and Optimal Region Selection” (5 citations) tackles the open problem of real-time footstep and motion synthesis under perception and state-estimation errors, pushing robots closer to deployment in demanding industrial settings. With a growing citation impact, Shim’s contributions are shaping the next generation of agile, perceptive robots. His work is essential reading for students and researchers interested in the intersection of optimization, learning, and control for dynamic locomotion.
Research Focus
Key Achievements
Top Papers
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- 2